Zuojing Su

For the purpose of interpreting and controlling the irregular yaw motion of very large ships in waves, both analytical and numerical methods are employed in this study. A nonlinear dynamic model considering the behaviors of the human navigator for the yawing system of the ships in regular longitudinal waves is constructed and analyzed based on the bifurcation and chaos theory. The results of analysis indicate the existence of chaotic regions for the system, interpreting the irregular yaw motion of very large ships. Furthermore, to mitigate the chaotic yaw phenomenon of the subject ship, a concise robust integrator backstepping algorithm, incorporating the close-loop gain shaping algorithm is proposed based on the nonlinear dynamic model. It possesses a concise form and is easily implementable in marine practice. Comparative numerical experiments are conducted to validate the effectiveness and superiority of the proposed algorithm.

To promise the high fidelity of path following control for the underactuated ship from the aspect of the nautical practice, this paper develops a nonlinear feedback-based control scheme with an improved compound line-of-sight (ICLOS) guidance law, which has the energy-saving and efficient performance. Based on the methodology of the nonlinear disturbance observer and method of reduced-order extended state observer (ESO), a reduced-order state nonlinear sideslip angle observer is developed for online estimation of the time-varying sideslip angel caused by external disturbances, and a sideslip angle controller is incorporated to avert the sharp acting of actuators. Meanwhile, unlike the traditional line-of-sight (LOS) guidance law, a time-varying lookahead distance is designed as a function that relates to the ship' cruising speed and the cross track error, while the advance steering distance is also variable responding to the ship's length and turning angle, both of them make the turning process smoother. In the control scheme, the second-order closed-loop gain shaping algorithm (CGSA) is employed to design a concise robust controller, where the fuzzy logic system (FLS) is introduced to adjust the integral coefficient online to improve the transient performance. In addition, to further reduce the steering frequency and energy consumption of the system, nonlinear feedback technology (NFT) is adopted. Numerical simulation and comprehensive comparisons are conducted to demonstrate the remarkable performance and superiority of the proposed path following control system.

In this note, a novel multitechnique concise robust control scheme, based on the mirror mapping technique (MMT), closed-loop gain shaping algorithm (CGSA), Smith predictor (SP), and nonlinear feedback technique (NFT), is proposed for the pressure control of the liquefied natural gas (LNG) carrier insulation containment space. Firstly, a kind of integral unstable time-delay model is obtained by linearizing the nonlinear model of the pressure maintenance system around its equilibrium. By using the MMT and CGSA, one acquires the corresponding stable mirror mapping model of the unstable linear model and the designed controller. And the SP is introduced to tackle the time-delay problem. In addition, for the purpose of energy saving, the NFT is added into the control scheme. Finally, a set of experiments has been employed to illustrate the control effects, and the results show that it can achieve satisfying performance in aspects of disturbance rejection and energy saving.

During the mission at sea, the yaw motion of the smart autonomous surface vessel (SASV) is always subject to the external disturbances. These issues make the design of a robust controller for steering autopilot system a quite challenging task. This paper focuses on the utilization of closed-loop shaping filter (CSF) to improve the SASV steering L2 gain robust control scheme. First, the mathematical model of the SASV is proposed and the parameters of the SASV model are obtained by the recursive least squares method (RLSM) based on the SASV turning test. Then the CSF L2 gain nonlinear robust controller design for SASV steering autopilot is described, and the stability and robustness of the proposed controller are proved based on the Lyapunov synthesis. Finally, the strong robustness and superior control performance are demonstrated through the practical SASV experiments. At the same time, the concise controller structure and definite physical meaning of SASV controller parameters are also concluded.

During the mission at sea, the ship steering control to yaw motions of the intelligent autonomous surface vessel (IASV) is a very challenging task. In this paper, a quantum neural network (QNN) which takes the advantages of learning capabilities and fast learning rate is proposed to act as the foundation feedback control hierarchy module of the IASV planning and control strategy. The numeric simulations had shown that the QNN steering controller could improve the learning rate performance significantly comparing with the conventional neural networks. Furthermore, the numeric and practical steering control experiment of the IASV BAICHUAN has shown a good control performance similar to the conventional PID steering controller and it confirms the feasibility of the QNN steering controller of IASV planning and control engineering applications in the future.